Physical Sciences and Mathematics Commons^™

Rfviz: An Interactive Visualization Package For Random Forests In R, Christopher Beckett

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Random forests are very popular tools for predictive analysis and data science. They work for both classification (where there is a categorical response variable) and regression (where the response is continuous). Random forests provide proximities, and both local and global measures of variable importance. However, these quantities require special tools to be effectively used to interpret the forest. Rfviz is a sophisticated interactive visualization package and toolkit in R, specially designed for interpreting the results of a random forest in a user-friendly way. Rfviz uses a recently developed R package (loon) from the Comprehensive R Archive Network (CRAN) to create …

Interactive Random Forests Plots, Anna T. Quach

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Random Forests is a useful data mining tool that is quite popular in finding variable importance. However, many people don’t make use of the Random Forests results in interactive graphs. Partly, this is because software packages that can do interactive graphs can’t handle large data sets and those that use Random Forests have large data sets or many variables. A new software package in R, known as iPlots eXtreme, that is still in development makes it simple to explore large data sets interactively. I have created a function, called irfplot (interactive random forests plot) that specifically uses Random Forests to …

Comparison Of Random Forests And Cforest: Variable Importance Measures And Prediction Accuracies, Rong Xia

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Random forests are ensembles of trees that give accurate predictions for regression, classification and clustering problems. The CART tree, the base learn er employed by random forests, has been criticized because of bias in the selection of splitting variables. The performance of random forests is suspect due to this criticism. A new implementation of random forests, Cforest, which is claimed to outperform random forests in both predictive power and variable importance measures , was developed based on Ctree, an implementation of conditional inference trees.

We address the underlying mechanism of random forests and Cforest in this report. Comparison of random …